Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study.

OBJECTIVE: To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making. DESIGN: Population-based cohort study (n = 35,997) included with a fall in 2013 and followed 1 year. METHODS: Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates. Multivariable logistic regression models were developed and internally validated (10-fold cross-validation). Performance was assessed via discrimination (area under the receiver operating characteristic curve, AUC), Brier scores, calibration and decision curve analysis. RESULTS: The AUC of age and sex for mortality was 72.5% [95% confidence interval 71.8 to 73.2]. The best model included age, sex, number of medications and health care spending DIORs. It exhibited high discrimination (AUC: 81.1 [80.5 to 81.6]), good calibration and potential clinical benefit for various threshold probabilities. Overall, health care spending patterns improved predictive accuracy the most while also exhibiting superior performance and clinical benefit. CONCLUSIONS: Patterns of health care spending have the potential to significantly improve assessments on who is at high risk of dying following admission to the ED with a fall. The proposed methodology can assist in predicting the prognosis of fallers, emphasising the added predictive value of longitudinal health-related information next to clinical and sociodemographic predictors.

Harnessing the digital potential of the next generation of health professionals.

Digital technologies are rapidly being integrated into a wide range of health fields. This new domain, often termed 'digital health', has the potential to significantly improve healthcare outcomes and global health equity more broadly. However, its effective implementation and responsible use are contingent on building a health workforce with a sufficient level of knowledge and skills to effectively navigate the digital transformations in health. More specifically, the next generation of health professionals-namely youth-must be adequately prepared to maximise the potential of these digital transformations. In this commentary, we highlight three priority areas which should be prioritised in digital education to realise the benefits of digital health: capacity building, opportunities for youth, and an ethics-driven approach. Firstly, capacity building requires educational frameworks and curricula to not only be updated, but to also place an emphasis on interdisciplinary learning. Secondly, opportunities are important for youth to meaningfully participate in decision-making processes and gain invaluable practical experiences. Thirdly, training in digital ethics and the responsible use of data as a standard component of education will help to safeguard against potential future inequities resulting from the implementation and use of digital health technologies.